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SNA-FGD(Family Group Detection in Social Network Analysis)

参考1：SNA相关会议期刊列表
参考2：CCF: 数据挖掘与内容检索相关会议期刊推荐列表
参考3：SC-Social Computing教程汇总

搜索关键词：
Community detection Community detection survey community discovery Detecting family  relationship labeling Social Matching Behavior Targeting graph mining Dense Subgraph Discovery

1.若干会议的期刊论文梳理

1.1 ICWSM: The International AAAI Conference on Web and Social Media

2016 topic相关梳理

  • 个人特征模型

    • 年龄预测
      Zhang J, Hu X, Zhang Y, et al. Your Age Is No Secret: Inferring Microbloggers’ Ages via Content and Interaction Analysis[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
    • 给朋友关系标签
      Park J Y, Sohn Y, Moon S. Power of Earned Advertising on Social Network Services: A Case Study of Friend Tagging on Facebook[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
    • 理解仇恨讨厌的情绪
      Silva L, Mondal M, Correa D, et al. Analyzing the Targets of Hate in Online Social Media[J]. arXiv preprint arXiv:1603.07709, 2016.
    • 根据用户使用的APP对用户进行画像
      Malmi E, Weber I. You Are What Apps You Use: Demographic Prediction Based on User's Apps[J]. arXiv preprint arXiv:1603.00059, 2016.
    • 根据twitter的数据定位家庭位置和活动位置
      Hossain N, Hu T, Feizi R, et al. Precise Localization of Homes and Activities: Detecting Drinking-While-Tweeting Patterns in Communities[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
    • 理解仇恨讨厌的情绪
      Jones I, Wang R, Han J, et al. Community Cores: Removing Size Bias from Community Detection[J]. 2015.

  • 社交推荐
    Celis L E, Krafft P M, Kobe N. Sequential Voting Promotes Collective Discovery in Social Recommendation Systems[J]. arXiv preprint arXiv:1603.04466, 2016.

  • 群体推荐
    Pramanik S, Gundapuneni M, Pathak S, et al. Predicting Group Success in Meetup[C]//Tenth International AAAI Conference on Web and Social Media. 2016.

  • 计算广告

    • 根据twitter的数据预测用户兴趣，如果给定一个广告、那么会分析出一个最佳的线下广告投放的区域
      Anagnostopoulos A, Petroni F, Sorella M. Targeted Interest-Driven Advertising in Cities Using Twitter[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
    • facebook，设备跨屏打通
      Coey D, Bailey M. People and Cookies: Imperfect Treatment Assignment in Online Experiments[C]//Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2016: 1103-1111.

2015 topic相关梳理

  • 挖掘隐含的用户特征
    Chen J, Haber E, Kang R, et al. Making use of derived personality: The case of social media ad targeting[C]//Proceedings of the International AAAI Conference on Web and Social Media (ICWSM). 2015.
  • 分析用户在现实与网络中的活动参与
    Hu Y, Farnham S, Talamadupula K. Predicting user engagement on twitter with real-world events[C]//Proceedings of the International Conference on Weblogs and Social Media (ICWSM). AAAI. 2015.
  • 根据照片分析家庭位置和旅游位置
    Zheng D, Hu T, You Q, et al. Towards lifestyle understanding: Predicting home and vacation locations from user’s online photo collections[C]//Proceedings of the 9th International AAAI Conference on Web and Social Media. 2015: 553-560.
  • 分析隐私行为
    Dong C, Jin H, Knijnenburg B P. Predicting privacy behavior on online social networks[C]//Proceedings of the AAAI Conference on Web and Social Media. 2015.
  • 基于位置的社交用户区分以及位置理解
    Rossi L, Williams M J, Stich C, et al. Privacy and the city: User identification and location semantics in location-based social networks[J]. arXiv preprint arXiv:1503.06499, 2015.
  • 分析用户的互动行为
    Carton S, Adar E, Park S, et al. Audience analysis for competing memes in social media[C]//Ninth International AAAI Conference on Web and Social Media. 2015.
  • 分析朋友间关系网络从三个方面影响着小群组团体
    Polonski V W, Hogan B. Assessing the Structural Correlates between Friendship Networks and Conversational Agency in Facebook Groups[C]//Ninth International AAAI Conference on Web and Social Media. 2015.

1.2 ASONAM: The IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

2016 topic相关梳理(全部的文章链接)

1.3 ISCI: International Conference on Social Computing

1.4 SOCIALCOM: The IEEE International Conference on Social Computing and Networking

1.5 CEC: IEEE Congress on Evolutionary Computation (CEC)

2015 topic相关梳理,偏算法相关论文的链接

  • 改进粒子群优化（PSO）算法，并将其应用在团体挖掘
    Cao C, Ni Q, Zhai Y. A novel community detection method based on discrete particle swarm optimization algorithms in complex networks[C]//2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015: 171-178.
  • Fiedler vector centrality方法进行deep community detect，本文是关于Fiedler vector的工作
    Wang S, Gong M, Shen B, et al. Deep community detection based on memetic algorithm[C]//2015 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2015: 648-655.

2014 topic相关梳理,偏算法相关论文的链接

  • 参考图模型的聚类划分，考虑network topology (Density, Centralization,Heterogeneity, Neighbourhood, Clustering Coefficient)进行图的划分
    Bello-Orgaz G, Camacho D. Evolutionary clustering algorithm for community detection using graph-based information[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 930-937.
  • 不需要先验知识的、非模块化的压缩算法
    Wu J, Yuan L, Gong Q, et al. A compression optimization algorithm for community detection[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 667-671.
  • 基于结构相似度进行划分的一种算法
    Mu C, Xie J, Liu R, et al. A memetic algorithm using local structural information for detecting community structure in complex networks[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 680-686.
  • 基于采样的蚁群聚类算法进行团体划分
    Song X, Ji J, Yang C, et al. Ant colony clustering based on sampling for community detection[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 687-692.
  • 处理用户交互特别多的场景下面的一种称为ECDA的划分算法
    He T, Chan K C C. Evolutionary community detection in social networks[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 1496-1503.
  • 蚁群聚类算法优化
    Mu C, Zhang J, Jiao L. An intelligent ant colony optimization for community detection in complex networks[C]//2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014: 700-706.

1.6 CSCW: The ACM Conference on Computer-Supported Cooperative Work and Social Computing(CCF-B 人机交互与普适计算)

1.7 TKDE: IEEE Transactions on Knowledge and Data Engineering(CCF-A 数据挖掘领域)

2016 topic相关梳理,偏算法相关论文的链接

  • 利用一种称为种子拓展的方法进行重叠类型的团体发掘
    Whang J J, Gleich D F, Dhillon I S. Overlapping community detection using neighborhood-inflated seed expansion[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(5): 1272-1284.
  • 区别于传统的图划分算法，因为不能确定明确的边界，基于任何一个小团体都是属于另外的团体的子集的事实本文提出一种新的方法
    Mahmood A, Small M. Subspace Based Network Community Detection Using Sparse Linear Coding[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(3): 801-812.

2015 topic相关梳理,偏算法相关论文的链接

  • 重叠社区快速检索算法
    Bandyopadhyay S, Chowdhary G, Sengupta D. FOCS: Fast Overlapped Community Search[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(11): 2974-2985.

2014 topic相关梳理,偏算法相关论文的链接

  • 基于时间平滑的多目标类基因算法进行团体挖掘
    Folino F, Pizzuti C. An evolutionary multiobjective approach for community discovery in dynamic networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1838-1852.
  • 多维数据格式的网络中团体挖掘
    Li X, Ng M K, Ye Y. Multicomm: Finding community structure in multi-dimensional networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(4): 929-941.
  • 方法集成：综合考虑结构和内容属性，讨论连接结构、节点的内容和边的含义进行团体划分
    Wang C D, Lai J H, Philip S Y. NEIWalk: community discovery in dynamic content-based networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1734-1748.
  • k-SDA算法进行匿名团体挖掘
    Tai C H, Philip S Y, Yang D N, et al. Structural diversity for resisting community identification in published social networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 235-252.
  • SP-tree算法解决团体的移动问题
    Zhu W Y, Peng W C, Hung C C, et al. Exploring Sequential Probability Tree for Movement-Based Community Discovery[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2717-2730.

1.8 IEEE/ACM Transactions on Networking(CCF-A 计算机网络)

2016 topic相关梳理,偏算法与网络理论相关论文的链接

  • 利用bootstrap percolation results 和 a novel graph slicing technique 来处理社交网络去匿名化
    Fabiana C. Social Network De-anonymization Under Scale-free User Relations[J]. IEEE-ACM TRANSACTIONS ON NETWORKING.

1.9 IEEE/WIC/ACM International Conference on Web Intelligence(CCF-C)链接

2015 topic相关梳理,偏应用相关论文的链接

  • Ramdom walk算法打标签进行团体挖掘
    Su C, Jia X, Xie X, et al. A New Random-Walk Based Label Propagation Community Detection Algorithm[C]//2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2015, 1: 137-140.
  • 问答网站兴趣重叠团体的挖掘
    Meng Z, Gandon F, Zucker C F. Simplified detection and labeling of overlapping communities of interest in question-and-answer sites[C]//2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2015, 1: 107-114.

1.10 SNA-KDD 2007-2014 (KDD WorkShop on SNA

2013 Accepted papers

2012 Accepted papers

  • 基于CRF(Conditional Random Fields)跨社交网络用户属性聚合
    Bartunov S, Korshunov A, Park S T, et al. Joint link-attribute user identity resolution in online social networks[C]//Proceedings of the 6th International Conference on Knowledge Discovery and Data Mining, Workshop on Social Network Mining and Analysis. ACM. 2012.
  • 基于通话记录推断用户间关系
    Motahari S, Mengshoel O J, Reuther P, et al. The impact of social affinity on phone calling patterns: categorizing social ties from call data records[C]//Proc. of the Sixth Workshop on Social Network Mining and Analysis. 2012.
  • 通话记录聚类分析
    Kurucz M, Benczur A, Csalogány K, et al. Spectral clustering in telephone call graphs[C]//Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, 2007: 82-91.

2011 Accepted papers

2.若干机构的期刊论文梳理

2.1 research.facebook.com

  • FaceBook中家庭程员的一些特征
    Burke M, Adamic L A, Marciniak K. Families on Facebook[C]//ICWSM. 2013.
  • FaceBook中不同性别用户的一些特征，比如女性用户获得的点赞评论等更多，类似的
    Wang Y C, Burke M, Kraut R E. Gender, topic, and audience response: an analysis of user-generated content on facebook[C]//Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 2013: 31-34.

3.论文list

3.1 SNA基础论文分类梳理

  • 超经典论文 Weak ties，引用40223
    GB/T 7714 Granovetter M S. The strength of weak ties[J]. American journal of sociology, 1973: 1360-1380.
  • 经典论文 Weak ties->structure
    Burt R S. Structural holes: The social structure of competition[M]. Harvard university press, 2009.
  • Newman M E J. Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality[J]. Physical review E, 2001, 64(1): 016132.
  • Christakis N A, Fowler J H. The spread of obesity in a large social network over 32 years[J]. New England journal of medicine, 2007, 357(4): 370-379.

3.2 Community/Group Detection基础论文分类梳理

  • 一般Community Detection
  • 综述（47页）：综述团体发现、评估、实验
    Chakraborty T, Dalmia A, Mukherjee A, et al. Metrics for Community Analysis: A Survey[J]. arXiv preprint arXiv:1604.03512, 2016.
  • 综述（48页）：有向网络中的聚类和团体挖掘
    Malliaros F D, Vazirgiannis M. Clustering and community detection in directed networks: A survey[J]. Physics Reports, 2013, 533(4): 95-142.
  • 综述（14页）：一共13种网络中挖掘团体的算法的对比
    Harenberg S, Bello G, Gjeltema L, et al. Community detection in large‐scale networks: a survey and empirical evaluation[J]. Wiley Interdisciplinary Reviews: Computational Statistics, 2014, 6(6): 426-439.
  • 综述（100页）：图模型中的团体挖掘
    Fortunato S. Community detection in graphs[J]. Physics reports, 2010, 486(3): 75-174.
  • 综述（35页）
    Coscia M, Giannotti F, Pedreschi D. A classification for community discovery methods in complex networks[J]. Statistical Analysis and Data Mining, 2011, 4(5): 512-546.
  • 综述（7页）
    Allahverdyan A E, Ver Steeg G, Galstyan A. Community detection with and without prior information[J]. EPL (Europhysics Letters), 2010, 90(1): 18002.
  • 综述（27页）
    Paxton N C, Russell S, Moskowitz I S, et al. A Survey of Community Detection Algorithms Based On Analysis-Intent[M]//Cyber Warfare. Springer International Publishing, 2015: 237-263.
  • 综述（15页）
    Cai Q, Ma L, Gong M, et al. A survey on network community detection based on evolutionary computation[J]. International Journal of Bio-Inspired Computation, 2016, 8(2): 84-98.
  • 综述（19页）
    Gregory S. Fuzzy overlapping communities in networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2011, 2011(02): P02017.
  • 综述（5页）
    Tamimi I, El Kamili M. Literature survey on dynamic community detection and models of social networks[C]//Wireless Networks and Mobile Communications (WINCOM), 2015 International Conference on. IEEE, 2015: 1-5.
  • 综述（12页）
    Kim J, Lee J G. Community detection in multi-layer graphs: A survey[J]. ACM SIGMOD Record, 2015, 44(3): 37-48.
  • 综述（22页）
    Liu, Guoliang. “Community Structure and Detection in Complex Networks: a Survey.” (2012).
  • 综述（2页）
    Hauptman A. Are Evolutionary Computation-Based Methods Comparable to State-of-the-art non-Evolutionary Methods for Community Detection?[C]//Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. ACM, 2016: 1465-1466.

3.3 Family Community/Group Detection on SNA综述性文章

  • 综述（40页）
    Papadopoulos S, Kompatsiaris Y, Vakali A, et al. Community detection in social media[J]. Data Mining and Knowledge Discovery, 2012, 24(3): 515-554.
  • 综述（137页）
    Tang L, Liu H. Community detection and mining in social media[J]. Synthesis Lectures on Data Mining and Knowledge Discovery, 2010, 2(1): 1-137.
  • 综述（20页）
    GB/T 7714 Plantié M, Crampes M. Survey on social community detection[M]//Social media retrieval. Springer London, 2013: 65-85.
  • 综述（28页）
    Kayastha N, Niyato D, Wang P, et al. Applications, architectures, and protocol design issues for mobile social networks: A survey[J]. Proceedings of the IEEE, 2011, 99(12): 2130-2158.

3.4 Family Community/Group Detection on SNA论文分类梳理

(1)如何得到用户特征（数据预处理类别：侧重于处理数据集，获取数据集，处理数据成可以分析的）

(2)用户特征建模（如何对数据建模：根据数据生成关系标签）

(3)用户间关系挖掘、群体发现（community/group detection 方法类别）

  • 本文有详细的笔记整理Wan H Y, Lin Y F, Wu Z H, et al. Discovering typed communities in mobile social networks[J]. Journal of Computer Science and Technology, 2012, 27(3): 480-491.

####(4)应用优化、如何利用已经得到的结论优化某个场景的应用 ####(5)评价指标一类

###3.5 Friends Detection on SNA

  • 利用朋友关系和家人圈更精准的挖掘用户间关系
    Zheleva E, Getoor L, Golbeck J, et al. Using friendship ties and family circles for link prediction[M]//Advances in Social Network Mining and Analysis. Springer Berlin Heidelberg, 2010: 97-113.

3.5 未整理列表

4 相关topic

4.1 图挖掘/频繁子图挖掘

5 相关的数据集

数据集名称       来源           链接 说明
https://www.kaggle.com/datasets   https://www.kaggle.com/datasets
SBP-BRiMS每年会发布challenge     SBP-BRiMS2016 challenge SBP-BRiMS2016 challenge 从SBP2016的一个ICEWS中存储的位置得到dataverse，中国复旦大学有个站点，但内容是社科的，在wiki中看到类似的站点有DSpace、 CKAN，但没有找到相关有用的数据集